基于掩码引导的潜在扩散的重构-偏移判别用于医学异常检测
Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection
AI总结：
该研究提出判别式掩码引导扩散（DMD）框架，结合自监督分类的重构-偏移判别与残差定位，在五种医学影像数据集上实现了最优的无监督医学异常检测性能。
AI中文摘要：
无监督医学异常检测从健康训练图像中学习正常解剖模式，并在测试时识别偏差。基于重构和扩散的方法通常将输入图像与其重构结果的差异作为异常证据，但这种残差可能存在歧义：表达性模型可能保留病理结构，而良性解剖变异、成像噪声和采集差异也可能产生较大的重构误差。我们提出判别式掩码引导扩散（Discriminative Mask-Guided Diffusion, DMD），这是一种医学异常检测框架，通过重构-偏移判别对基于残差的定位进行补充。DMD首先学习正常图像的紧凑量化潜在表示，然后用局部掩码扰动选定的潜在区域，再由潜在扩散模型重构受扰动的表示；将得到的重构结果与其原始正常图像配对，定义一个自监督分类任务。推理时，分类器提供学习到的图像级异常评分，而输入与其基于扩散的重构结果之间的残差则生成像素级异常图。在涵盖脑部MRI、乳腺超声和胸部X线摄影的五个数据集上进行的实验表明，DMD在所有最先进的基线方法中实现了最佳的整体性能。
英文摘要：
Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time. Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence. However, this residual can be ambiguous. Expressive models may preserve pathological structures, while benign anatomical variation, imaging noise, and acquisition differences may also produce large reconstruction errors. We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination. DMD first learns a compact quantized latent representation of normal images. Localized masks then perturb selected latent regions, and a latent diffusion model reconstructs the perturbed representations. The resulting reconstructions are paired with their original normal images to define a self-supervised classification task. At inference, the classifier provides a learned image-level anomaly score, while the residual between the input and its diffusion-based reconstruction yields a pixel-level anomaly map. Experiments on five datasets spanning brain MRI, breast ultrasound, and chest radiography show that DMD achieves the best overall performance among the state-of-the-art baseline methods.
